Top Machine Learning Jobs in Toronto

As Director of Sales, you will drive revenue growth through new and existing customer acquisition, manage the sales cycle, and develop strategies for market expansion in the pharmaceutical sector.

The Senior/Staff AI Researcher will lead research in Generative AI, focusing on model improvement, algorithm development, and product integration while managing cross-functional collaborations and monitoring industry trends.

As Chief Product & Technology Officer, you will lead technology and product strategy, oversee product lifecycle, and scale teams at FutureFit AI to support workforce development.

As a Senior Backend Engineer, you'll own the algorithms and infrastructure for ads systems, collaborating with Data Science to deploy ML models and enhance marketplace efficiency.

The Data and ML Engineer will gather, transform data, develop ML solutions, optimize pipelines, ensure data quality, and collaborate on AI product design while participating in a two-year catalyst program.

As a Staff Applied AI/ML Scientist, you will lead the development of search and recommendation systems, integrating advanced ML techniques to enhance e-commerce experiences.

As a Senior Site Reliability Engineer, you'll manage HPC infrastructure, optimize operations, automate tasks, and support engineering and research teams.

Develop enterprise ML platform, manage Kubernetes clusters, build tools for ML operations, and collaborate with teams to meet specifications.

The Staff Full Stack Engineer will lead the architecture and implementation of scalable AI learner engagement systems, mentor other engineers, and contribute to technical direction and best practices.

Lead NerdWallet's customer support transformation through AI, overseeing operations, strategy, and team development to improve customer satisfaction and operational efficiency.

As a Staff Machine Learning Engineer, you will design scalable AI infrastructure, implement AI systems, lead technical decisions, and collaborate with teams to enhance AI/ML solutions.

As an ML Engineer, you will develop and deploy machine learning solutions, design robust pipelines, and collaborate with cross-functional teams to improve key performance indicators in the travel industry.

The role involves designing scalable distributed systems using Scala, enhancing feature stores for performance, and collaborating with ML engineers to resolve challenges.

Drive the vision and architecture for ML infrastructure, tackling complex technical challenges and mentoring engineers while collaborating with cross-functional teams.

Lead the design and development of advanced ML systems focusing on NLP and LLM. Manage a team of engineers and ensure project delivery aligns with product goals.

The Principal Product Manager leads product lifecycle management for brand safety solutions using ML/AI, ensuring effective collaboration and communication across teams.

The Junior Data Scientist will design and develop data models using Machine Learning and Analytics for trading and compliance in finance.

The Machine Learning Engineer will scale core infrastructure, manage data pipelines and model evaluation, and improve benchmarking systems. Collaborate with teams to enhance AI evaluation methodologies.

As a Senior Product Manager, you will own product outcomes, develop roadmaps, translate market insights into action, and navigate technical complexities in a startup environment.

Deliver compelling presentations and host discussions on emerging AI topics, engaging with senior leaders to translate AI concepts into strategic insights.

The Manager, Field Engineering will lead a team of Solutions Architects, driving customer success with Databricks' data and AI technologies while collaborating with Sales and enhancing team culture.

The Product Owner will translate strategic goals into user stories, manage the development backlog, facilitate agile ceremonies, and ensure feature quality for the product roadmap.

As an AI/ML Engineer, you will design and implement agentic systems, develop multi-agent workflows, and collaborate with senior engineers to optimize AI techniques.

Design and deliver scalable machine learning infrastructure and pipelines for AI/ML models, ensuring data integrity and collaborating across teams.

Design and maintain ML pipelines and data pipelines, optimize fraud detection models, collaborate with teams, and ensure data quality for high-scale applications.